Industry Growth Analysis with BERT ( tapas-base-finetuned-wtq)by Aniket Artani

Welcome to the Industry Growth Analysis code documentation! This code aims to help users analyze the growth percentages of various industries. Using a fine-tuned BERT model, the code allows users to find the industry with the highest or lowest growth on a given day or retrieve the growth percentage of a specific industry.

Problem Statement

The problem addressed by this code is to enable users to access and analyze industry growth data easily. Users should be able to perform the following tasks:

Find the industry with the highest growth percentage on a given time.
Find the industry with the lowest growth percentage on a given day.
Retrieve the growth percentage of a particular industry on a given day.

Solution Overview

To solve the problem, we employed a BERT fine-tuned model, which is a state-of-the-art natural language processing model. We use this model to analyze our data.And we used Beautiful soup to extract relevant information(during runtime) about industry growth from various sources. The key libraries used in this solution are:

Transformers: A powerful library by Hugging Face that provides pre-trained NLP models, including BERT.
Pandas: A versatile library for data manipulation and analysis.
Beautiful Soup: A library for extracting data from HTML and XML files.

Dependencies

Before running the code, please ensure you have the following dependencies installed:
Python 3.x
transformers (Hugging Face)
pandas
beautifulsoup4

How to run


conda create --name textbase
conda activate textbase
conda install pip
pip install poetry
poetry install
poetry run python textbase/textbase_cli.py test main.py

Conclusion

This code enables users to analyze industry growth data using a fine-tuned BERT model. By providing options to find the industry with the highest or lowest growth percentage on a given day and retrieve the growth percentage of a specific industry, users can gain valuable insights into the current state of various industries.

References

Here are some references that might be helpful in understanding the components used in this code:

Hugging Face Transformers: https://huggingface.co/transformers/
Pandas Documentation: https://pandas.pydata.org/docs/
Beautiful Soup Documentation: https://www.crummy.com/software/BeautifulSoup/bs4/doc/
Remember to cite and credit the respective sources if you use any external information or libraries in your implementation.

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chatbot-mercor's Introduction

Industry Growth Analysis with BERT ( tapas-base-finetuned-wtq)by Aniket Artani

Problem Statement

Solution Overview

Dependencies

How to run

Conclusion

References

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